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    <article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/scaling-data-to-the-standard-normal.html" class="u-url">scaling-data-to-the-standard-normal</a></h1>
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            <p class="byline author vcard"><span class="byline-name fn">
                Tao Junjie
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            <p class="dateline"><a href="posts/scaling-data-to-the-standard-normal.html" rel="bookmark"><time class="published dt-published" datetime="2015-07-27T14:58:39+08:00" title="2015-07-27 14:58">2015-07-27 14:58</time></a></p>
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<h2 id="把数据调整为标准正态分布">把数据调整为标准正态分布<a class="anchor-link" href="posts/scaling-data-to-the-standard-normal.html#%E6%8A%8A%E6%95%B0%E6%8D%AE%E8%B0%83%E6%95%B4%E4%B8%BA%E6%A0%87%E5%87%86%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83">¶</a>
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<p>经常需要将数据标准化调整（scaling）为标准正态分布（standard normal）。标准正态分布算得上是统计学中最重要的分布了。如果你学过统计，Z值表（z-scores）应该不陌生。实际上，Z值表的作用就是把服从某种分布的特征转换成标准正态分布的Z值。</p>
<p class="more"><a href="posts/scaling-data-to-the-standard-normal.html">Read more…</a></p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/reducing-dimensionality-with-pca.html" class="u-url">reducing-dimensionality-with-pca</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/reducing-dimensionality-with-pca.html" rel="bookmark"><time class="published dt-published" datetime="2015-07-27T14:58:33+08:00" title="2015-07-27 14:58">2015-07-27 14:58</time></a></p>
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<h2 id="用主成分分析降维">用主成分分析降维<a class="anchor-link" href="posts/reducing-dimensionality-with-pca.html#%E7%94%A8%E4%B8%BB%E6%88%90%E5%88%86%E5%88%86%E6%9E%90%E9%99%8D%E7%BB%B4">¶</a>
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<p>现在是时候升一级了！主成分分析（Principal component analysis，PCA）是本书介绍的第一个高级技术。到目前为止都是些简单的统计学知识，而PCA将统计学和线性代数组合起来实现降维，堪称简单模型的杀手锏。</p>
<p class="more"><a href="posts/reducing-dimensionality-with-pca.html">Read more…</a></p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/putting-it-all-together-with-pipelines.html" class="u-url">putting-it-all-together-with-pipelines</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/putting-it-all-together-with-pipelines.html" rel="bookmark"><time class="published dt-published" datetime="2015-07-27T14:58:28+08:00" title="2015-07-27 14:58">2015-07-27 14:58</time></a></p>
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<h2 id="用管线命令连接多个转换方法">用管线命令连接多个转换方法<a class="anchor-link" href="posts/putting-it-all-together-with-pipelines.html#%E7%94%A8%E7%AE%A1%E7%BA%BF%E5%91%BD%E4%BB%A4%E8%BF%9E%E6%8E%A5%E5%A4%9A%E4%B8%AA%E8%BD%AC%E6%8D%A2%E6%96%B9%E6%B3%95">¶</a>
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<p>下面，让我们用管线命令连接多个转换方法，来演示一个复杂点儿的例子。</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/kernel-pca-for-nonlinear-dimensionality-reduction.html" class="u-url">kernel-pca-for-nonlinear-dimensionality-reduction</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/kernel-pca-for-nonlinear-dimensionality-reduction.html" rel="bookmark"><time class="published dt-published" datetime="2015-07-27T14:58:22+08:00" title="2015-07-27 14:58">2015-07-27 14:58</time></a></p>
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<h2 id="用核PCA实现非线性降维">用核PCA实现非线性降维<a class="anchor-link" href="posts/kernel-pca-for-nonlinear-dimensionality-reduction.html#%E7%94%A8%E6%A0%B8PCA%E5%AE%9E%E7%8E%B0%E9%9D%9E%E7%BA%BF%E6%80%A7%E9%99%8D%E7%BB%B4">¶</a>
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<p>由于大多数统计方法最开始都是线性的，所以，想解决非线性问题，就需要做一些调整。PCA也是一种线性变换。本主题将首先介绍它的非线性形式，然后介绍如何降维。</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/imputing-missing-values-through-various-strategies.html" class="u-url">imputing-missing-values-through-various-strategies</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/imputing-missing-values-through-various-strategies.html" rel="bookmark"><time class="published dt-published" datetime="2015-07-27T14:58:16+08:00" title="2015-07-27 14:58">2015-07-27 14:58</time></a></p>
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<h2 id="处理缺失值">处理缺失值<a class="anchor-link" href="posts/imputing-missing-values-through-various-strategies.html#%E5%A4%84%E7%90%86%E7%BC%BA%E5%A4%B1%E5%80%BC">¶</a>
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<p>实践中数值计算不可或缺，好在有很多方法可用，这个主题将介绍其中一些。不过，这些方法未必能解决你的问题。</p>
<p>scikit-learn有一些常见的计算方法，它可以对现有数据进行变换填补<code>NA</code>值。但是，如果数据集中的缺失值是有意而为之的——例如，服务器响应时间超过100ms——那么更合适的方法是用其他包解决，像处理贝叶斯问题的PyMC，处理风险模型的lifelines，或者自己设计一套方法。</p>
<p class="more"><a href="posts/imputing-missing-values-through-various-strategies.html">Read more…</a></p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/getting-sample-data-from-external-sources.html" class="u-url">getting-sample-data-from-external-sources</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/getting-sample-data-from-external-sources.html" rel="bookmark"><time class="published dt-published" datetime="2015-07-27T14:58:10+08:00" title="2015-07-27 14:58">2015-07-27 14:58</time></a></p>
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<h2 id="从外部源获取样本数据">从外部源获取样本数据<a class="anchor-link" href="posts/getting-sample-data-from-external-sources.html#%E4%BB%8E%E5%A4%96%E9%83%A8%E6%BA%90%E8%8E%B7%E5%8F%96%E6%A0%B7%E6%9C%AC%E6%95%B0%E6%8D%AE">¶</a>
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<p>如果条件允许，学本书内容时尽量用你熟悉的数据集；方便起见，我们用scikit-learn的内置数据库。这些内置数据库可用于测试不同的建模技术，如回归和分类。而且这些内置数据库都是非常著名的数据库。这对不同领域的学术论文的作者们来说是很用的，他们可以用这些内置数据库将他们的模型与其他模型进行比较。</p>
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<p>推荐使用IPython来运行文中的指令。大内存很重要，这样可以让普通的命令正常运行。如果用IPython Notebook就更好了。如果你用Notebook，记得用<code>%matplotlib inline</code>指令，这样图象就会出现在Notebook里面，而不是一个新窗口里。</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/defining-the-gaussian-process-object-directly.html" class="u-url">defining-the-gaussian-process-object-directly</a></h1>
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            <p class="byline author vcard"><span class="byline-name fn">
                Tao Junjie
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            <p class="dateline"><a href="posts/defining-the-gaussian-process-object-directly.html" rel="bookmark"><time class="published dt-published" datetime="2015-07-27T14:58:04+08:00" title="2015-07-27 14:58">2015-07-27 14:58</time></a></p>
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<h2 id="直接定义一个正态随机过程对象">直接定义一个正态随机过程对象<a class="anchor-link" href="posts/defining-the-gaussian-process-object-directly.html#%E7%9B%B4%E6%8E%A5%E5%AE%9A%E4%B9%89%E4%B8%80%E4%B8%AA%E6%AD%A3%E6%80%81%E9%9A%8F%E6%9C%BA%E8%BF%87%E7%A8%8B%E5%AF%B9%E8%B1%A1">¶</a>
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<p>前面我们只触及了正态随机过程的表面。在本主题中，我们将介绍直接创建一个具有指定相关函数的正态随机过程。</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/decomposition-to-classify-with-dictionarylearning.html" class="u-url">decomposition-to-classify-with-dictionarylearning</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/decomposition-to-classify-with-dictionarylearning.html" rel="bookmark"><time class="published dt-published" datetime="2015-07-27T14:57:58+08:00" title="2015-07-27 14:57">2015-07-27 14:57</time></a></p>
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<h2 id="用字典学习分解法分类">用字典学习分解法分类<a class="anchor-link" href="posts/decomposition-to-classify-with-dictionarylearning.html#%E7%94%A8%E5%AD%97%E5%85%B8%E5%AD%A6%E4%B9%A0%E5%88%86%E8%A7%A3%E6%B3%95%E5%88%86%E7%B1%BB">¶</a>
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<p>在这个主题中，我们将介绍一种可以用于分类的分解方法——字典学习（Dictionary Learning），将数据集转换成一个稀疏的形式。</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/creating-sample-data-for-toy-analysis.html" class="u-url">creating-sample-data-for-toy-analysis</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/creating-sample-data-for-toy-analysis.html" rel="bookmark"><time class="published dt-published" datetime="2015-07-27T14:57:52+08:00" title="2015-07-27 14:57">2015-07-27 14:57</time></a></p>
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<h2 id="创建试验样本数据">创建试验样本数据<a class="anchor-link" href="posts/creating-sample-data-for-toy-analysis.html#%E5%88%9B%E5%BB%BA%E8%AF%95%E9%AA%8C%E6%A0%B7%E6%9C%AC%E6%95%B0%E6%8D%AE">¶</a>
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<p>希望你在学习本书时用自己的数据来试验，如果实在没有数据，下面就介绍如何用scikit-learn创建一些试验用的样本数据（toy data）。</p>
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    </article><article class="h-entry post-text"><header><h1 class="p-name entry-title"><a href="posts/creating-binary-features-through-thresholding.html" class="u-url">creating-binary-features-through-thresholding</a></h1>
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                Tao Junjie
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            <p class="dateline"><a href="posts/creating-binary-features-through-thresholding.html" rel="bookmark"><time class="published dt-published" datetime="2015-07-27T14:57:47+08:00" title="2015-07-27 14:57">2015-07-27 14:57</time></a></p>
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<h2 id="用阈值创建二元特征">用阈值创建二元特征<a class="anchor-link" href="posts/creating-binary-features-through-thresholding.html#%E7%94%A8%E9%98%88%E5%80%BC%E5%88%9B%E5%BB%BA%E4%BA%8C%E5%85%83%E7%89%B9%E5%BE%81">¶</a>
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<p>在前一个主题，我们介绍了数据转换成标准正态分布的方法。现在，我们看看另一种完全不同的转换方法。</p>
<p>当不需要呈标准化分布的数据时，我们可以不处理它们直接使用；但是，如果有足够理由，直接使用也许是聪明的做法。通常，尤其是处理连续数据时，可以通过建立二元特征来分割数据。</p>
<p class="more"><a href="posts/creating-binary-features-through-thresholding.html">Read more…</a></p>
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